• Collaborative Research Project

• Review

• Static maps with ggmap

• Dynamic results presentation

  • Static website hosting with gh-pages

  • Shiny Server

Purpose: Pose an interesting research question and try to answer it using data analysis and standard academic practices. Effectively communicate your results to a variety of audiences in a variety of formats.

Deadline:

• Presentation: In-class Friday 5 December

• Website/Paper: 12 December

The project is a 'dry run' for your thesis with multiple presentation outputs.

Presentation: 10 minutes maximum. Engagingly present your research question and key findings to a general academic audience (fellow students).

Paper: 6,000 words maximum. Standard academic paper, properly cited laying out your research question, literature review, data and methods, and findings.

Website: An engaging website designed to convey your research to a general audience.

As always, you should submit one GitHub repository with all of the materials needed to completely reproduce your data gathering, analysis, and presentation documents.

Note: Because you've had two assignments already to work on parts of the project, I expect high quality work.

• What is the data-ink ratio? Why is it important for effective plotting.

• What is visual weighting?

• Why should you avoid using the size of circles to mean anything in a plot?

• How many decimal places should you report in a table?

Last class we didn't have time to cover mapping with ggmap.

We've already seen how ggmap can be used to find latitude and longitude.

library(ggmap)

places <- c('Bavaria', 'Seoul', '6 Parisier Platz, Berlin',
            'Hertie School of Governance')

geocode(places)

##         lon      lat
## 1  11.49789 48.79045
## 2 126.97797 37.56654
## 3  13.37854 52.51701
## 4  13.38921 52.51286

qmap(location = 'Berlin', zoom = 15)

Example from: Kahle and Wickham (2013)

Use crime data set that comes with ggmap

names(crime)

##  [1] "time"     "date"     "hour"     "premise"  "offense"  "beat"    
##  [7] "block"    "street"   "type"     "suffix"   "number"   "month"   
## [13] "day"      "location" "address"  "lon"      "lat"

# find a reasonable spatial extent
qmap('houston', zoom = 13) # gglocator(2) see in RStudio

# only violent crimes
violent_crimes <- subset(crime,
    offense != "auto theft" & offense != "theft" &
    offense != "burglary")

# order violent crimes
violent_crimes$offense <- factor(violent_crimes$offense,
    levels = c("robbery", "aggravated assault", "rape", "murder"))

# restrict to downtown
violent_crimes <- subset(violent_crimes,
    -95.39681 <= lon & lon <= -95.34188 &
    29.73631 <= lat & lat <= 29.78400)

# Set up base map
HoustonMap <- qmap("houston", zoom = 14, color = "bw",
                legend = "topleft")

# Add points
FinalMap <- HoustonMap +
                geom_point(aes(x = lon, y = lat, colour = offense,
                               size = offense),
                data = violent_crimes)

print(FinalMap)

When your output documents are in HTML, you can create interactive visualisations.

Potentially more engaging and could let users explore data on their own.

Big distinction:

Client Side: Plots are created on the user's (client's) computer. Often JavaScript in the browser. You simply send them static HTML/JavaScript needed for their browser to create the plots.

Server Side: Data manipulations and/or plots (e.g. with Shiny Server) are done on a server.

There are lots of free services (e.g. GitHub Pages) for hosting webpages for client side plot rendering.

You usually have to use a paid service for server side data manipulation plotting.

You already know how to create HTML documents with R Markdown.

There are a growing set of tools for interactive plotting:

• googleVis

• dygraphs

• rcharts

• ggvis

• networkD3

You can use the googleVis package to create Google plots from R.

Example from googleVis Vignettes.

# Create fake data
fake_compare <- data.frame(
                country = c("US", "GB", "BR"),
                val1 = c(10,13,14),
                val2 = c(23,12,32))

library(googleVis)
line_plot <- gvisLineChart(fake_compare)
print(line_plot, tag = 'chart')

Note: Uses `results='asis' in the code chunk head.

To show the in R use plot instead of print and don't include tag = 'chart'.

library(WDI)
co2 <- WDI(indicator = 'EN.ATM.CO2E.PC', start = 2010, end = 2010)
co2 <- co2[, c('iso2c','EN.ATM.CO2E.PC')]

# Clean
names(co2) <- c('iso2c', 'CO2 Emmissions per Capita')
co2[, 2] <- round(log(co2[, 2]), digits = 2)

co2_map <- gvisGeoMap(co2, locationvar = 'iso2c',
                      numvar = 'CO2 Emmissions per Capita',
                      options = list(
                          colors = '[0xfff7bc, 0xfec44f,
                                    0xd95f0e]'
                          ))

Note: That 0x replaces # for hexadecimal colors.

CO2 Emmissions (metric tons per capita)

print(co2_map, tag = 'chart')

Note: you will need to view googleVis maps that are in R Markdown documents in your browser rather than RStudio's built in HTML viewer.

Use the dygraphs package to plot interactive time series using the dygraphs JavaScript library.

Not on CRAN yet, so install from GitHub

devtools::install_github(c("ramnathv/htmlwidgets",
                           "rstudio/dygraphs"))

library(dygraphs)
lungDeaths <- cbind(mdeaths, fdeaths)
dygraph(lungDeaths)

dygraph(lungDeaths) %>% dyRangeSelector()